Transfer learning has demonstrated efficacy in single-property constraint molecular generation. However, real-world drug discovery demands molecules to satisfy multiple property constraints. Existing paradigms often struggle with this challenge due to catastrophic forgetting or gradient conflicts. To address this, we propose a conflict-aware molecular language model merging framework (CAML). CAML generates multiple constraints molecular as a cooperative game among property-specific fine-tune models (expert models). Specifically, we formulate a Stability-Aware Covariance Matrix Adaptation Evolution Strategy (SACMA-ES) to dynamically optimize the fusion strategy. This algorithm searches for a Nash-equilibrium–like solution that minimizes conflicts among properties by exploring the optimal combination of the importance of the task parameter (intrinsic scale) and relative fusion weights of each expert (fusion coefficient), yielding a multi-constraint molecular property generation model without revisiting the training data. Extensive experiments demonstrate that CAML achieves state-of-the-art performance in complex multi-constraint scenarios. Our results validate that this training-free paradigm offers a robust and efficient solution for resolving intrinsic property conflicts in de novo molecular design.

Aligning Large Language Models (LLMs) to human preferences is pivotal for Machine Translation (MT), yet current approaches are often hindered by misleading reward signals. Our analysis reveals that prevailing Quality Estimation (QE) models exhibit a systematic blind spot towards **partial errors**—specifically partial hallucinations and omissions—often favoring superficially fluent but unfaithful translations. To address this, we propose **M²PO** (**M**ulti-Perspective **M**ulti-Pair **P**reference **O**ptimization), a data-centric framework for preference optimization in machine translation. First, to correct the bias towards fluency, M²PO uses a multi-perspective alignment mechanism that decouples semantic fidelity from fluency, prioritizing faithfulness via a curriculum strategy. Second, with the bias corrected, partial errors fall between perfect and severely incorrect translations, making them inefficient to learn via standard best-versus-worst comparisons. We thus introduce a multi-pair objective that leverages the full candidate list to capture these fine-grained error signals. Experiments on WMT23, WMT24, and FLORES-200 show that M²PO enables a 9B model to outperform leading open-source baselines and achieve parity with proprietary models like GPT-4o and Gemini-2.0-Flash, demonstrating significant potential for efficient, high-fidelity LLM-based translation.

Drug repurposing plays a critical role in accelerating treatment discovery, especially for complex and rare diseases. Biomedical knowledge graphs (KGs), which encode rich clinical associations, have been widely adopted to support this task. However, existing methods largely overlook common-sense biomedical concept knowledge in real-world labs, such as mechanistic priors indicating that certain drugs are fundamentally incompatible with specific treatments. To address this gap, we propose LLaDR, a Large Language Model-assisted framework for Drug Repurposing, which improves the representation of biomedical concepts within KGs. Specifically, we extract semantically enriched treatment-related textual representations of biomedical entities from large language models (LLMs) and use them to fine-tune knowledge graph embedding (KGE) models. By injecting treatment-relevant knowledge into KGE, LLaDR largely improves the representation of biomedical concepts, enhancing semantic understanding of under-studied or complex indications. Experiments based on benchmarks demonstrate that LLaDR achieves state-of-the-art performance across different scenarios, with case studies on Alzheimer’s disease further confirming its robustness and effectiveness.

Predicting the types and affinities of protein-protein interactions (PPIs) is crucial for understanding biological processes and developing novel therapeutic approaches. While encoding proteins themselves is essential, PPI networks can also provide rich prior knowledge for these predictive tasks. However, existing methods oversimplify the problem of PPI prediction in a semi-supervised manner when utilizing PPI networks, limiting their practical application. Furthermore, how to effectively use the rich prior knowledge of PPI networks for novel proteins not present in the network remains an unexplored issue. Additionally, due to inflexible architectures, most of existing methods cannot handle complexes containing an flexible number of proteins. To overcome these limitations, we introduce LLaPA (Large Language and Protein Assistant), a multimodal large language model that integrates proteins and PPI networks. LLaPA offers a more rational approach to utilizing PPI networks for PPI prediction and can fully exploit the information of PPI networks for unseen proteins. Through natural language instructions, LLaPA can accept flexible number of protein sequences and has the potential to perform various protein tasks. Experiments show that LLaPA achieves state-of-the-art performance in multi-label PPI (mPPI) type prediction and is capable of predicting the binding affinity between multiple interacting proteins based on sequence data.